Design and implementation of a privacy-preserving data aggregation framework
Table Of Contents
<p>1. Introduction<br> 1.1 Background<br> 1.2 Objectives<br> 1.3 Importance of privacy-preserving data aggregation<br>2. Literature Review<br> 2.1 Data aggregation challenges<br> 2.2 Privacy-preserving techniques<br>3. Framework Design<br> 3.1 Cryptographic protocols for secure aggregation<br> 3.2 Differential privacy mechanisms<br> 3.3 Secure multi-party computation<br>4. Implementation<br> 4.1 Software and hardware requirements<br> 4.2 Integration with data sources<br>5. Security Analysis<br> 5.1 Threat modeling<br> 5.2 Privacy guarantees and limitations<br></p>
Project Abstract
This project aims to design and implement a privacy-preserving data aggregation framework that enables secure and efficient aggregation of sensitive data from multiple sources while preserving the privacy and confidentiality of individual data contributors. The framework will employ cryptographic techniques, differential privacy, and secure multi-party computation to facilitate privacy-preserving data aggregation for various applications, such as healthcare analytics, financial reporting, and statistical analysis.
Project Overview
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